[MUSIC PLAYING]
MOLLY DELANEY: Regardless of what type of data analysis
you're conducting, the process is generally the same.
The example that I'll walk through
is that of our employee engagement survey.
But you can imagine that this process applies to just about
any data analysis that you're going to conduct as an analyst.
The first thing you want to do is ask.
You want to ask all of the right questions
at the beginning of the engagement
so that you better understand what
your leaders and stakeholders need from this analysis.
So the types of questions that I generally ask
are around, what is the problem that we're trying to solve?
What is the purpose of this analysis?
What are we hoping to learn from it?
So after you've asked all the right questions,
and you've wrapped your arms around the scope
of the analysis you need to conduct,
the next step is to prepare.
We need to be thinking about what type of data
we need to answer those key questions.
This could be anything from quantitative data
or qualitative data.
It could be cross-sectional or point in time,
versus longitudinal, over a long period of time.
We need to be thinking about the type of data
we need in order to answer the questions that we've set out
to answer based on what we learned when
we asked the right questions.
We also need to be thinking about how
we're going to collect that data or if we
need to collect that data.
It may be the case that we need to collect this data brand new,
and so we need to think about what type of data
we're going to be collecting and how.
For our employee engagement survey,
we do that via survey of both quantitative and qualitative
questions.
But it may actually be the case that, for many analyses,
the data that you're looking for already exist.
Then it's a question of working with those data owners
to make sure that you're able to leverage that data
and use it responsibly.
After you've done all the hard work to collect your data,
now you need to process that data.
It begins with cleaning.
This, to me, is the most fun part
of the data analytics process.
We can think of it as the initial introduction
or the handshake, hello to your data.
This is where you get a chance to understand
its structure, its quirks, its nuances,
and you really get a chance to understand deeply
what type of data you're going to be
working with and understanding what potential that data
has to answer all of your questions.
This is such an important part too,
where we're running through all of our quality-assurance
checks.
For example, do we have all of the data
that we anticipated we would have?
Are we missing data at random?
Or is it missing in a systematic way, such
that maybe something went wrong with our data collection
effort?
If needed, did we code all of our data the right way?
Are there any outliers that we need to treat differently?
This is the part where you spend a lot of time really digging
deeply into the structure and nuance of the data
to make sure that you're able to analyze it
appropriately and responsibly.
After cleaning our data and running
all of our quality-assurance checks,
now is the point where we analyze our data,
making sure to do so in as objective and unbiased way
as possible.
To do this, the first thing we do
is run through a series of analyses
that we've already planned ahead of time based on the questions
that we know we want to answer from the very, very beginning
of the process.
One thing that's probably the hardest
about this particular process, the hardest thing analyzing
data, is that we as analysts are trained to look for patterns.
And over time, as we become better and better
at our jobs, what we'll often find
is that we can start to intuit what we might see in the data.
We might have a sneaking suspicion as to what
the data are going to tell us.
And this is the point where we have
to take a step back and let the data speak for itself.
As data analysts, we are storytellers,
but we also have to keep in mind that it is not
our story to tell.
That story belongs to the data, and it
is our job as analysts to amplify and tell
that story in as unbiased and objective a way as possible.
The next step is to share all of the data and insights
that you generated from your analyses.
Now, typically, for our employee engagement survey,
we start by sharing the high-level findings
with our executive team.
We want them to have a landscape view of how
the organization is feeling.
And we want to make sure that there aren't any surprises
as they dig deeper and deeper into the data
to understand how teams are feeling
and how individual employees are feeling.
All of this work, from asking the right questions
to collecting your data to analyzing and sharing,
doesn't mean much of anything if we aren't taking action
on what we've just learned.
This, to me, is the most critical part,
especially of our employee engagement survey.
I like to say that the survey is actually the easy part,
and acting on the results is really
where the real work begins.
This is where we use all of those data-driven insights
to decide what types of interventions
we want to introduce, not only at the organizational level,
but also at the team level as well.
So we might find, for example, that the organization
is working on a series of interventions
to help improve part of the employee experience,
whereas individual teams have additional
roles/responsibilities to play to either bolster some
of those efforts or to introduce new ones to better meet
their team where their strengths and opportunity areas are.
The data analysis process is rigorous, but it is lengthy,
and I can completely appreciate that we as data analysts
get so excited about just diving right into the data
and doing what we do best.
The challenge is that if we don't
work through the process in its entirety,
if we try to skip steps, we're not
going to be able to elicit the insights that we're
looking for.
I absolutely love my job.
I have such a deep appreciation for data and what it can do
and what type of insight we can derive from it.
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